What question did this study set out to answer?

The study aims to enhance small object detection in UAV imagery by employing advanced neural network techniques.

April 30, 2026Open Access

Enhancing small object detection in UAV imagery through edge-preserving context fusion

FYFeng YuChengdu University LXLijuan XiongChengdu University HZHaotian ZhouChengdu University

Key Points

The study aims to enhance small object detection in UAV imagery by employing advanced neural network techniques.
Developed ECF-YOLO, an enhanced YOLOv11n network with three modules: LoGStem, Direction-Aware Context Block, and Efficient Path Aggregation Neck.
Evaluated performance on the TinyPerson and VisDrone2019 datasets to assess detection improvements.
Detected performance improved on the TinyPerson dataset with mAP@0.5 rising from 14.3% to 22.2%.
Significant enhancement in detection accuracy observed across datasets.

Abstract

Unmanned Aerial Vehicles (UAVs) are pivotal in tasks such as traffic monitoring and rescue operations, yet detecting small objects in UAV images remains challenging due to their diminutive size, low resolution, and complex backgrounds. To address this, we propose ECF-YOLO, an enhanced network based on YOLOv11n, featuring three core modules: LoGStem for preserving geometric edges, the Direction-Aware Context Block for enhancing contextual understanding, and the Efficient Path Aggregation Neck for optimizing multi-scale feature fusion. Experimental results on the TinyPerson and VisDrone2019 datasets demonstrate significant improvements in detection performance, with mAP@0.5 increasing from 14.3% to 22.2% on the TinyPerson dataset. This study highlights the importance of edge preservation and contextual modeling in enhancing small object detection in UAV imagery. The source code will be available at https://github.com/YuIO26/ECF-YOLO

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